ML Anthology

De Novo Molecular and Crystal Design with Latent Space Bayesian Optimization

Onur Boyar
AAAI 2025 pp. 29247-29248
doi:10.1609/AAAI.V39I28.35201 /aaai/2025/boyar2025aaai-de/

Abstract

This thesis explores Latent Space Bayesian Optimization (LSBO) for the generation and optimization of de novo molecules and crystal materials. Our goal is to develop practical, sample-efficient de novo discovery algorithms with a focus on real-world applicability, and our results so far demonstrate significant progress toward practical implementation.

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Cite

Text

Boyar. "De Novo Molecular and Crystal Design with Latent Space Bayesian Optimization." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I28.35201

Markdown

[Boyar. "De Novo Molecular and Crystal Design with Latent Space Bayesian Optimization." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/boyar2025aaai-de/) doi:10.1609/AAAI.V39I28.35201

BibTeX

@inproceedings{boyar2025aaai-de,
  title     = {{De Novo Molecular and Crystal Design with Latent Space Bayesian Optimization}},
  author    = {Boyar, Onur},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2025},
  pages     = {29247-29248},
  doi       = {10.1609/AAAI.V39I28.35201},
  url       = {https://mlanthology.org/aaai/2025/boyar2025aaai-de/}
}